Although FL brings several advantages for edge intelligence systems, the existing FL framework still potentially experiences various adversarial attacks, such as membership inference and poisoning attacks [1]}. Here, in the membership inference attack, attackers might perform reverse engineering to gather user's private data by leveraging the updated model training, whereas a poisoning attack aims to affect the global model by sending the malicious updated models during the collaborative training phase. Furthermore, the central aggregator that is responsible for managing the whole system orchestration has trouble addressing crucial challenges associated with a Single Point of Failure (SPoF) issue, which may result in the whole FL system failure and lead to the risk of exposing private data. As a result, the users might be hesitant to participate in improving FL-based edge intelligence systems for VNs.
